University Quantum Mechanics I · topic UQMI-09.03
The power series, its recursion, and why it must terminate
Connect the ideas, make your working visible, and test the result. Use the hints when you need a starting point; open the full solution after your attempt.
Further practice
Work through a fresh case before tackling the extension.
The power series, its recursion, and why it must terminate: predict a finite change
The series method writes h(ξ) = Σ aₖξᵏ and generates every coefficient from the one two places below it; an energy is allowed only if that chain can be made to stop, because an unstopped chain grows so fast that ψ cannot be normalised. Use the supplied topic model Y_out = sqrt(ep), where Y_out is TURNING POINTS ±√ε (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x. ep is Dimensionless energy ε = 2E/ħω, dimensionless. Keep a0 (Even seed a₀) = 1; a1 (Odd seed a₁) = 0 fixed. For this exercise use only 1.24375 ≤ ep ≤ 6.75625 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. Calculate Y_out at ep = 2.4565 and 3.22825. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δep across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.
TURNING POINTS ±√ε against ep
Predictions from the supplied The power series, its recursion, and why it must terminate model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): TURNING POINTS ±√ε
Horizontal axis (x): ep: Dimensionless energy ε = 2E/ħω
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| ep: Dimensionless energy ε = 2E/ħω | TURNING POINTS ±√ε |
|---|---|
| 1.24375 | 1.1152354011597732 |
| 2.6218749999999997 | 1.6192204914711275 |
| 3.9999999999999996 | 1.9999999999999998 |
| 5.378124999999999 | 2.319078480776362 |
| 6.75625 | 2.599278746114006 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
Substitute each value of ep separately while keeping every other input fixed, then subtract in the same order.
Reveal the worked solution
- At ep = 2.4565, substitute into Y_out = sqrt(ep): Y₀ = 1.56732.
- At ep = 3.22825, the same fixed-parameter model gives Y₁ = 1.79673.
- Signed change: ΔY_out = Y₁ − Y₀ = 0.229411; the input change is Δep = 0.77175.
- Average sensitivity is ΔY_out/Δep = 0.29726 1 per unit input. Its positive sign means that this response increases over the specified interval.
- This is a secant slope between two settings. It equals a point derivative only for a locally linear relation or in a justified small-interval limit.
Answer Y₀ = 1.56732, Y₁ = 1.79673; ΔY_out = 0.229411; average sensitivity = 0.29726 1 per unit input.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Extra challenging questions
Work through the linked stages and explain what each result means.
The power series, its recursion, and why it must terminate: infer an input with uncertainty
The series method writes h(ξ) = Σ aₖξᵏ and generates every coefficient from the one two places below it; an energy is allowed only if that chain can be made to stop, because an unstopped chain grows so fast that ψ cannot be normalised. Use the supplied topic model Y_out = sqrt(ep), where Y_out is TURNING POINTS ±√ε (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x. ep is Dimensionless energy ε = 2E/ħω, dimensionless. Keep a0 (Even seed a₀) = 1; a1 (Odd seed a₁) = 0 fixed. For this exercise use only 1.24375 ≤ ep ≤ 6.75625 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 2.1462467, with a bounded reading uncertainty ±0.067955. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown ep by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00055125, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
TURNING POINTS ±√ε against ep
Predictions from the supplied The power series, its recursion, and why it must terminate model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): TURNING POINTS ±√ε
Horizontal axis (x): ep: Dimensionless energy ε = 2E/ħω
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| ep: Dimensionless energy ε = 2E/ħω | TURNING POINTS ±√ε |
|---|---|
| 1.24375 | 1.1152354011597732 |
| 2.6218749999999997 | 1.6192204914711275 |
| 3.9999999999999996 | 1.9999999999999998 |
| 5.378124999999999 | 2.319078480776362 |
| 6.75625 | 2.599278746114006 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(ep) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(1.24375) = 1.11524 and Y_out(6.75625) = 2.59928. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives ep_line = 1.24375 + (2.14625 − 1.11524)(6.75625 − 1.24375)/(2.59928 − 1.11524) = 5.07346. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives ep = 4.60637; substituting back gives Y_out = 2.1462467.
- The interpolation error in the input is |ep_line − ep| = 0.467081. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 2.0782917 and 2.2142017. Sort the roots even if the response decreases: 4.3193 ≤ ep ≤ 4.90269.
- The lower and upper input deviations are 0.287079 and 0.296314. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer ep ≈ 4.60637; compatible interval [4.3193, 4.90269]. Endpoint interpolation gives 5.07346.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.